// does not alter the last dimension and it restores the batch dimensions
// collapsed by the FullyConnected op due to `keep_num_dims=false`. For example,
//
//   // %input: tensor<4x16x32xf32>
//   %fc = tfl.fully_connected(%input, %filter, %bias)
//           {keep_num_dims = false, weights_format = "DEFAULT"}
//   %shape = arith.constant dense<[4, 16, 32]> : tensor<3xi32>

}

func.func @einsum_with_runtime_shape1(%arg0 : tensor<?x36x?xf32>, %arg1 : tensor<?x36x?x32xf32>) -> tensor<?x36x32xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {device = "", equation = "bin,binj->bij"} : (tensor<?x36x?xf32>, tensor<?x36x?x32xf32>) -> tensor<?x36x32xf32>
  func.return %0 : tensor<?x36x32xf32>
// CHECK-LABEL: einsum_with_runtime_shape1
// CHECK-DAG: %[[cst:.*]] = arith.constant dense<[0, 1, 3]> : tensor<3xi32>

    func.func @main(%a : tensor<5x14x1xf32>, %b : tensor<1x14x32xf32>) -> tensor<?x?x?xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "i,j", outputs = "identity_RetVal"}} {
      %c = "mhlo.einsum"(%a, %b) {einsum_config = "bji,bjk->bik"} : (tensor<5x14x1xf32>, tensor<1x14x32xf32>) -> tensor<?x?x?xf32>
      return %c : tensor<?x?x?xf32>
    }
  })";

}

// -----

func.func @Int64SliceBeginSize(%arg0: tensor<4x128x32xf32>) -> tensor<1x128x32xf32> {
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi64>} : () -> tensor<3xi64>
  %1 = "tfl.pseudo_const"() {value = dense<[1, 128, 32]> : tensor<3xi64>} : () -> tensor<3xi64>
  %2 = "tfl.slice"(%arg0, %0, %1) : (tensor<4x128x32xf32>, tensor<3xi64>, tensor<3xi64>) -> tensor<1x128x32xf32>
  func.return %2 : tensor<1x128x32xf32>

  %4 = "tf.Reshape"(%3, %1) {device = ""} : (tensor<2x4x4x2xf32>, tensor<3xi32>) -> tensor<2x16x2xf32>
  %5 = "tf.PartitionedCall"(%arg0, %4) {_collective_manager_ids = [], _read_only_resource_inputs = [], config = "", config_proto = "\0A\07\0A\03CPU\10\01\0A\07\0A\03GPU\10\002\02J\008\01\82\01\00", executor_type = "", f = @__inference_interpolate_bilinear} : (tensor<2x4x4x1xf32>, tensor<2x16x2xf32>) -> tensor<2x16x1xf32>

  // CHECK: [[RESHAPE:%.*]] = mhlo.reshape [[SLICE]] : (tensor<2x16x2xf32>) -> tensor<2x16x2xf32>
  %0 = "tf.StridedSlice"(%input, %begin, %end, %strides) {Index = i32, T = f32} : (tensor<10x16x2xf32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<2x16x2xf32>
  // CHECK: return [[RESHAPE]] : tensor<2x16x2xf32>
  func.return %0 : tensor<2x16x2xf32>
}

// -----